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New research finds temperature scaling fails on soft labels

By PulseAugur Editorial · [2 sources] ·

A new research paper challenges the effectiveness of temperature scaling for model calibration, particularly when dealing with soft or distributional human labels. The study found that temperature scaling, which assumes deterministic one-hot labels, consistently underperforms an oracle calibrated directly on soft labels. This calibration gap is larger in language models compared to vision models and tends to increase with model scale. The findings suggest that current calibration methods may misrepresent model reliability in safety-critical applications where label ambiguity is inherent. AI

IMPACT Calibration protocols built on majority-vote labels may systematically misstate model reliability in safety-critical settings.

RANK_REASON Academic paper on model calibration methods.

Read on arXiv cs.LG →

AI-generated summary · Google Gemini · from 2 sources. How we write summaries →

New research finds temperature scaling fails on soft labels

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Academic paper on model calibration methods.
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COVERAGE [2]

  1. arXiv cs.LG TIER_1 English(EN) · Wisdom Dogah ·

    Temperature Scaling Is Not Enough: Calibration Gaps Under Human Label Distributions

    arXiv:2607.13423v1 Announce Type: new Abstract: Temperature scaling is the dominant post-hoc calibration method in modern deep learning. Its theoretical justification rests on an assumption that is rarely stated explicitly: that ground-truth labels are one-hot and deterministic. …

  2. arXiv cs.LG TIER_1 English(EN) · Wisdom Dogah ·

    Temperature Scaling Is Not Enough: Calibration Gaps Under Human Label Distributions

    Temperature scaling is the dominant post-hoc calibration method in modern deep learning. Its theoretical justification rests on an assumption that is rarely stated explicitly: that ground-truth labels are one-hot and deterministic. In practice, labels are frequently soft, crowd-s…

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